Kudva, Yogish C.

Background: Safe and effective glucose control in the intensive care unit (ICU) continues to be actively pursued. Large clinical trials have examined the safety and efficacy of insulin infusion protocols in medical and surgical ICUs. We report experiences of a single-center standardized nurse-driven insulin infusion protocol in three ICUs in an observational quality-improvement study. Method: We analyzed the hourly point-of-care arterial blood glucose obtained during ICU insulin infusion protocol (protocol A) with a glucose target of 80–130 mg/dl in medical and surgical ICUs in February 2009. Following Normoglycemia in Intensive Care Evaluation and Survival Using Glucose Algorithm Regulation (NICE-SUGAR) study results, the protocol was amended (protocol B) to achieve target glucose of 110–150 mg/dl. The performance of protocol B was assessed in the ICUs in May 2010 and compared with protocol A with respect to glucose concentrations and rates of severe (<40 mg/dl) and moderate (40–60 mg/dl) hypoglycemia. Results: With protocol A, in medical ( n = 44) and surgical ( n = 164) ICUs taken together, median glucose was 119 mg/dl, with severe and moderate hypoglycemia rates 1.4% (3/208) and 7.7% (16/208), respectively, which were significantly lower than those reported by the NICE-SUGAR and the Leuven studies. With protocol B, in medical ( n = 44) and surgical ( n = 167) ICUs taken together, median glucose was 132 mg/dl, with severe and moderate hypoglycemia of 0 % (0/211) and 0.5% (1/211), respectively. Conclusion: The current ICU insulin infusion protocol (protocol B) reduces severe and moderate hypoglycemia without compromising glucose control when compared with protocol A. This could potentially impact patient-important outcomes.

Objective: We conducted a systematic review and meta-analysis to assess the efficacy of continuous glucose monitoring (CGM) in improving glycemic control and reducing hypoglycemia compared to self-monitored blood glucose (SMBG). Methods: We searched MEDLINE, EMBASE, Cochrane Central, Web of Science, and Scopus for randomized trials of adults and children with type 1 or type 2 diabetes mellitus (T1DM or T2DM). Pairs of reviewers independently selected studies, assessed methodological quality, and extracted data. Meta-analytic estimates of treatment effects were generated using a random-effects model. Results: Nineteen trials were eligible and provided data for meta-analysis. Overall, CGM was associated with a significant reduction in mean hemoglobin A1c [HbA1c; weighted mean difference (WMD) of −0.27% (95% confidence interval [CI] −0.44 to −0.10)]. This was true for adults with T1DM as well as T2DM [WMD −0.50% (95% CI −0.69 to −0.30) and −0.70 (95% CI, −1.14 to −0.27), respectively]. No significant effect was noted in children and adolescents. There was no significant difference in HbA1c reduction between studies of real-time versus non-real-time devices (WMD −0.22%, 95% CI, −0.59 to 0.15 versus −0.30%, 95% CI, −0.49 to −0.10; p for interaction 0.71). The quality of evidence was moderate due to imprecision, suggesting increased risk for bias. Data for the incidence of severe or nocturnal hypoglycemia were sparse and imprecise. In studies that reported patient satisfaction, users felt confident about the device and gave positive reviews. Conclusion: Continuous glucose monitoring seems to help improve glycemic control in adults with T1DM and T2DM. The effect on hypoglycemia incidence is imprecise and unclear. Larger trials with longer follow-up are needed to assess the efficacy of CGM in reducing patient-important complications without significantly increasing the burden of care for patients with diabetes.

OBJECTIVE The goal was to develop a new index of insulin sensitivity in patients with type 1 diabetes estimated from continuous glucose monitoring (CGM) and subcutaneous insulin delivery data under carefully controlled conditions. RESEARCH DESIGN AND METHODS The database consists of 12 subjects with type 1 diabetes, studied during breakfast, lunch, and dinner, in a clinical research unit, wearing both subcutaneous insulin pump and CGM device. Frequent blood samples were drawn for measurements of plasma glucose and insulin concentrations in order to estimate insulin sensitivity with the oral minimal model (SIMM). The new index of insulin sensitivity (SISP) was calculated with a simple algebraic formula for each meal, using only CGM and insulin pump data and compared with SIMM. RESULTS SI SP was well correlated with SIMM (r = 0.825; P &lt; 10−8), and diurnal pattern was also similar to SIMM. CONCLUSIONS A novel method for estimating insulin sensitivity in subjects with type 1 diabetes on sensor-augmented insulin pump therapy has been presented. This new index correlates well with the reference oral minimal model estimate of insulin sensitivity. The knowledge of patient-specific insulin sensitivity and its diurnal variation can help in optimizing insulin therapy in type 1 diabetes and could also inform next-generation closed-loop control systems.

The accuracy of continuous interstitial fluid (ISF) glucose sensing is an essential component of current and emerging open- and closed-loop systems for type 1 diabetes. An important determinant of sensor accuracy is the physiological time lag of glucose transport from the vascular to the interstitial space. We performed the first direct measurement of this phenomenon to our knowledge in eight healthy subjects under an overnight fasted condition. Microdialysis catheters were inserted into the abdominal subcutaneous space. After intravenous bolus administrations of glucose tracers, timed samples of plasma and ISF were collected sequentially and analyzed for tracer enrichments. After accounting for catheter dead space and assay noise, the mean time lag of tracer appearance in the interstitial space was 5.3–6.2 min. We conclude that in the overnight fasted state in healthy adults, the physiological delay of glucose transport from the vascular to the interstitial space is 5–6 min. Physiological delay between blood glucose and ISF glucose, therefore, should not be an obstacle to sensor accuracy in overnight or fasting-state closed-loop systems of insulin delivery or open-loop therapy assessment for type 1 diabetes.

Background: In type 1 diabetes mellitus (T1DM), physical activity (PA) lowers the risk of cardiovascular complications but hinders the achievement of optimal glycemic control, transiently boosting insulin action and increasing hypoglycemia risk. Quantitative investigation of relationships between PA-related signals and glucose dynamics, tracked using, for example, continuous glucose monitoring (CGM) sensors, have been barely explored. Subjects and Methods: In the clinic, 20 control and 19 T1DM subjects were studied for 4 consecutive days. They underwent low-intensity PA sessions daily. PA was tracked by the PA monitoring system (PAMS), a system comprising accelerometers and inclinometers. Variations on glucose dynamics were tracked estimating first- and second-order time derivatives of glucose concentration from CGM via Bayesian smoothing. Short-time effects of PA on glucose dynamics were quantified through the partial correlation function in the interval (0, 60 min) after starting PA. Results: Correlation of PA with glucose time derivatives is evident. In T1DM, the negative correlation with the first-order glucose time derivative is maximal (absolute value) after 15 min of PA, whereas the positive correlation is maximal after 40–45 min. The negative correlation between the second-order time derivative and PA is maximal after 5 min, whereas the positive correlation is maximal after 35–40 min. Control subjects provided similar results but with positive and negative correlation peaks anticipated of 5 min. Conclusions: Quantitative information on correlation between mild PA and short-term glucose dynamics was obtained. This represents a preliminary important step toward incorporation of PA information in more realistic physiological models of the glucose–insulin system usable in T1DM simulators, in development of closed-loop artificial pancreas control algorithms, and in CGM-based prediction algorithms for generation of hypoglycemic alerts.

Background: Currently, patients with type 1 diabetes decide on the amount of insulin to administer based on several factors, including current plasma glucose value, expected meal input, and physical activity (PA). One future therapeutic modality for patients with type 1 diabetes is the artificial endocrine pancreas (AEP). Incorporation of PA could enhance the efficacy of AEP significantly. We compared the main technologies used for PA quantitation. Subjects and Methods: Data were collected during inpatient studies involving healthy control subjects and type 1 diabetes. We report PA quantified from accelerometers (acceleration units [AU]) and heart rate (HR) monitors during a standardized activity protocol performed after a dinner meal at 7 p.m. from nine control subjects (four were males, 37.4±12.7 years old, body mass index of 24.8±3.8 kg/m 2 , and fasting plasma glucose of 4.71±0.63 mmol/L) and eight with type 1 diabetes (six were males, 45.2±13.4 years old, body mass index of 25.1±2.9 kg/m 2 , and fasting plasma glucose of 8.44±2.31 mmol/L). Results: The patient-to-patient variability was considerably less when examining AU compared with HR monitors. Furthermore, the exercise bouts and rest periods were more evident from the data streams when AUs were used to quantify activity. Unlike the AU, the HR measurements provided little insight for active and rest stages, and HR data required patient-specific standardizations to discern any meaningful pattern in the data. Conclusions: Our results indicated that AU provides a reliable signal in response to PA, including low-intensity activity. Correlation of this signal with continuous glucose monitoring data would be the next step before exploring inclusion as input for AEP control.

Summary Streptozotocin (STZ), a glucosamine-nitrosourea compound, has potent genotoxic effects on pancreatic β-cells and is frequently used to induce diabetes in experimental animals. Glucagon-like peptide-1 (GLP-1) has β-cell protective effects and is known to preserve β-cells from STZ treatment. In this study, we analyzed the mechanisms of STZ-induced diabetes and GLP-1-mediated β-cell protection in STZ-treated mice. At 1 week after multiple low-dose STZ administrations, pancreatic β-cells showed impaired insulin expression, while maintaining expression of nuclear Nkx6.1. This was accompanied by significant upregulation of p53-responsive genes in islets, including a mediator of cell cycle arrest, p21 (also known as Waf1 and Cip1). STZ treatment also suppressed expression of a wide range of genes linked with key β-cell functions or diabetes development, such as G6pc2, Slc2a2 (Glut2), Slc30a8, Neurod1, Ucn3, Gad1, Isl1, Foxa2, Vdr, Pdx1, Fkbp1b and Abcc8, suggesting global β-cell defects in STZ-treated islets. The Tmem229B, Prss53 and Ttc28 genes were highly expressed in untreated islets and strongly suppressed by STZ, suggesting their potential roles in β-cell function. When a pancreas-targeted adeno-associated virus (AAV) vector was employed for long-term Glp-1 gene delivery, pancreatic GLP-1 expression protected mice from STZ-induced diabetes through preservation of the β-cell mass. Despite its potent β-cell protective effects, however, pancreatic GLP-1 overexpression showed limited effects on the global gene expression profiles in the islets. Network analysis identified the programmed-cell-death-associated pathways as the most relevant network in Glp-1 gene therapy. Upon pancreatic GLP-1 expression, upregulation of Cxcl13 and Nptx2 was observed in STZ-damaged islets, but not in untreated normal islets. Given the pro-β-cell-survival effects of Cxcl12 (Sdf-1) in inducing GLP-1 production in α-cells, pancreatic GLP-1-mediated Cxcl13 induction might also play a crucial role in maintaining the integrity of β-cells in damaged islets.

Background and Purpose— Moyamoya disease is an uncommon, cerebrovascular occlusive disease of unknown pathogenesis. Previously described Moyamoya cohorts include predominantly Asian populations or ethnically diverse North American cohorts. To gain further insight into the pathogenesis of moyamoya, we examined clinical characteristics of a primarily white, Midwestern US population Methods— Retrospective analysis of patients with angiographically confirmed moyamoya disease evaluated at our institution was performed. Prevalence of comorbidities, cerebrovascular risk factors, and autoimmune diseases were compared with the general population. Results— Ninety-four patients with moyamoya were evaluated; 72.3% were female. Ethnic composition was primarily white (85%). A significantly higher prevalence of autoimmune disease was seen, particularly type 1 diabetes mellitus (8.5% versus 0.4% in the general population) and thyroid disease (17.0% versus 8.0% in the institutional general patient population). Hyperlipidemia was also increased (27.7% versus 16.3% in the general population). Conclusions— This study of a unique, primarily white, Midwestern population of moyamoya patients demonstrates a significantly higher prevalence of autoimmune disease than in the general population. This supports a possible autoimmune component to the pathogenesis of moyamoya disease.